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A Short-Term Stock Exchange Prediction Model Using Box-Jenkins Approach

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Journal of Applied Mathematics and Physics 2020年第5期8卷 766-779页

作者： Paul Boye yao yevenyo ziggah Mathematical Sciences Department University of Mines and Technology (UMaT) Tarkwa Ghana Geomatic Engineering Department University of Mines and Technology (UMaT) Tarkwa Ghana

This paper developed a short-term stock exchange prediction model using the Box-Jenkins approach. In this study, monthly data from Ghana Stock Exchange market report that spans from March 2013 to February 2018 were used to develop the model. ARIMA (0, 2, 1) model was fitted to the data based on the Bayesian Information Criterion (BIC) for model selection. Diagnostic checks showed that the residuals of the fitted model were uncorrelated. The developed model was used for forecasting for a period of six months. The trend of the forecasted values showed a significant increase in the Ghana Stock Exchange performance for the next six months.

关键词： ARIMA Bayesian Information Criterion Ghana Stock Exchange Perfor-mance Indicator

A Hybrid Model to Predict Cartesian Planimetric Coordinates

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Journal of Geodesy and Geomatics Engineering 2016年第1期3卷 48-61页

作者： Bernard Kumi-BoatengI yao yevenyo ziggah Department of Geomatic Engineering University of Mines and Technology Ghana Department of Survey and Mapping China University of Geosciences Wuhan China

Coordinates are basic needs for both geospatial and non-geospatial professionals and as a result, geodesists have the responsibility to develop methods that are applicable and practicable for determining cartesian coordinates either through transformation, conversion or prediction for the geo-scientific community. It is therefore necessary to implement mechanisms and systems that can be employed to predict coordinates in either two dimensional （2D） or three dimensional （3D） spaces. Artificial Intelligence （AI） techniques and conventional methods within the last decade have been proposed as an effective tool for modeling and forecasting in various scientific disciplines for solving majority of problems. The primary objective of this work is to compare the efficiency of artificial intelligence technique （Feed Forward Back propagation Neural Network （FFBPNN）） and conventional methods （Ordinary Least Squares （OLS）, General Least Squares （GLS）, and Total Least Squares （TLS）） in cartesian planimetric coordinate＇s prediction. In addition, a hybrid approach of conventional and artificial intelligence method thus, TLS-FFBPNN has been proposed in this study for 2D cartesian coordinates prediction. It was observed from the results obtained that FFBPNN performed significantly better than the conventional methods. However, the TLS-FFBPNN when compared with FFBPNN, OLS, GLS and TLS gave stronger and better performance and superior predictions. To further confirm the superiority of the TLS-FFBPNN the Bayesian Information Criterion was introduced. The BIC selected the TLS-FFBPNN as the optimum model for prediction.

关键词： Artificial intelligence ordinary least squares total least squares general least squares cartesian coordinates

Evaluation of Convolutional-Long Short-Term Memory Model in Predicting Surface Displacement of Underground Mines

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Advances in Remote Sensing 2024年第3期13卷 73-88页

作者： Saviour Mantey yao yevenyo ziggah Isaac Selasi Kojo Attipoe Department of Geomatic Engineering University of Mines and Technology Tarkwa Ghana

Surface stability is essential in underground mines health management systems. Unexpected Surface displacement in underground mines could lead to loss of lives, injuries, and economic losses. To reduce or neutralise the adverse effects of surface displacement, it is vital to monitor and accurately predict them and unravel their mechanisms. In recent years, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) have proven effective in predicting complex problems. However, CNN neglects the dynamic dependency of the input in the temporal dimension, which affects surface displacement features. The Convolutional-LSTM model can dynamically learn the temporal dependency among input features via the feedback connections in the LSTM to improve accurate captures of surface displacement features. This study focused on evaluating the C-LSTM model in predicting surface displacement of underground mines and assessed the predictive capabilities and generalisation strength of using hybridised ANN models. Geodetic and geotechnical data gathered from a Gold Mine in Ghana was used. The three models were tested on experimental data collected at Monitoring Scan Point 3. It was observed from the prediction output that all the methods could provide applicable and practical results. However, using indicators like root mean square error (RMSE) and correlation coefficient (R) in assessing the output of the prediction, the C-LSTM had the best prediction output. This study contributes to the advancement of accurate and efficient prediction of surface displacement of underground mines, ultimately enhancing and assisting safety operations.

关键词： ANN CNN LSTM C-LSTM Underground Mines Surface Displacement

A breakthrough of printable mesoscopic perovskite solar cells

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Science China Materials 2025年第1期68卷 307-308页

作者： Zhiyi yao Zhijun Ning School of Physical Science and Technology ShanghaiTech UniversityShanghai 201210China

High-performance solar cells based on low-cost wet coating methods are the much-anticipated technology for the next generation of photovoltaics. Perovskite solar cells (PSCs) provide a fascinating choice because their... 详细信息

关键词： perovskite breakthrough absorber

New results on finite-time stability and instability theorems for stochastic nonlinear time-varying systems

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Science China(Information Sciences) 2025年第2期68卷 186-199页

作者： Weihai ZHANG Liqiang yao College of Electrical Engineering and Automation Shandong University of Science and Technology School of Mathematics and Information Science Yantai University

This paper studies finite-time stability and instability theorems in the probability sense for stochastic nonlinear timevarying systems. Firstly, a new sufficient condition is proposed to guarantee that the considered system has a global ***, we propose new finite-time stability and instability criteria that relax the constraints on LV(the infinitesimal operator of Lyapunov function V) by the uniformly asymptotically stable function. On the one hand, these obtained results make up for the shortcomings of the existing results. On the other hand, the new finite-time stability theorems can be viewed as natural extensions of the existing results and also allow LV to be indefinite(negative or positive) rather than just only allow LV < ***, some simulation examples verify the validity of the theoretical results.

关键词： stochastic nonlinear systems finite-time stability finite-timeinstability uniformly asymptotically stable function

Neural Network Based on Inter-layer Perturbation Strategy for Text Classification

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Machine Intelligence Research 2025年第1期22卷 176-188页

作者： Nai Zhou Nianmin yao Qibin Li School of Computer Science and Technology Dalian University of TechnologyDalian116024China

Recently,many researches have created adversarial samples to enrich the diversity of training data for improving the text classification performance via reducing the loss incurred in the neural network ***,existing studies have focused solely on adding perturbations to the input,such as text sentences and embedded representations,resulting in adversarial samples that are very similar to the original *** adversarial samples can not significantly improve the diversity of training data,which restricts the potential for improved classification *** alleviate the problem,in this paper,we extend the diversity of generated adversarial samples based on the fact that adding different disturbances between different layers of neural network has different *** propose a novel neural network with perturbation strategy(PTNet),which generates adversarial samples by adding perturbation to the intrinsic representation of each hidden layer of the neural ***,we design two different perturbation ways to perturb each hidden layer:1)directly adding a certain threshold perturbation;2)adding the perturbation in the way of adversarial *** above settings,we can get more perturbed intrinsic representations of hidden layers and use them as new adversarial samples,thus improving the diversity of the augmented training *** validate the effectiveness of our approach on six text classification datasets and demonstrate that it improves the classification ability of the *** particular,the classification accuracy on the sentiment analysis task improved by an average of 1.79%and on question classification task improved by 3.2%compared to the BERT baseline,respectively.

关键词： Perturbation strategy adversarial training machine learning text classification adversarial samples

Impulsive thrust strategy for orbital pursuit-evasion games based on impulse-like constraint

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Chinese Journal of Aeronautics 2025年第1期38卷 520-536页

作者： Hongbo WANG yao ZHANG Hao LIU Kunpeng ZHANG School of Aerospace Engineering Beijing Institute of TechnologyBeijing 100081China National Key Laboratory of Space Intelligent Control Beijing Institute of Control EngineeringBeijing 100094China

This paper proposes a novel impulsive thrust strategy guided by optimal continuous thrust strategy to address two-player orbital pursuit-evasion game under impulsive thrust *** strategy seeks to enhance the interpretability of impulsive thrust strategy by integrating it within the framework of differential game in traditional continuous ***,this paper introduces an impulse-like constraint,with periodical changes in thrust amplitude,to characterize the impulsive thrust ***,the game with the impulse-like constraint is converted into the two-point boundary value problem,which is solved by the combined shooting and deep learning method proposed in this *** learning and numerical optimization are employed to obtain the guesses for unknown terminal adjoint variables and the game terminal ***,the accurate values are solved by the shooting method to yield the optimal continuous thrust strategy with the impulse-like ***,the shooting method is iteratively employed at each impulse decision moment to derive the impulsive thrust strategy guided by the optimal continuous thrust *** examples demonstrate the convergence of the combined shooting and deep learning method,even if the strongly nonlinear impulse-like constraint is *** effect of the impulsive thrust strategy guided by the optimal continuous thrust strategy is also discussed.

关键词： Orbital pursuit-evasion game Differential game Impulsive thrust Deep learning Shooting method

Dynamic Event-Triggered Active Disturbance Rejection Formation Control for Constrained Underactuated AUVs

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 460-462页

作者： Zhiguang Feng Sibo yao IEEE the College of Intelligent Systems Science and Engineering Harbin Engineering University

Dear Editor,This letter addresses the formation control problem for constrained underactuated autonomous underwater vehicles (AUVs). The feasibility condition of the virtual control law is eliminated by introducing a nonlinear state dependence function (NSDF) that transforms the state of each AUV in the formation.

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Sharp Isolated Toughness Bound for Fractional(k,m)-Deleted Graphs

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Acta Mathematicae Applicatae Sinica 2025年第1期41卷 252-269页

作者： Wei GAO Wei-fan WANG yao-jun CHEN School of Mathematics Hohai UniversityNanjing 210098China Department of Mathematics Zhejiang Normal UniversityJinhua 321004China Department of Mathematics Nanjing UniversityNanjing 210093China

A graph G is a fractional(k,m)-deleted graph if removing any m edges from G,the resulting subgraph still admits a fractional *** k≥2 and m≥1 be ***[2m/k]^(*)=[2m/k]if 2m/k is not an integer,and[2m/k]^(*)=[2m/k]-1 if 2m/k is an *** this paper,we prove that G is a fractional(k,m)-deleted graph if δ(G)≥k+m and isolated toughness meets I(G)>{3-1/m,if k=2 and m≥3,k+[2m/k]^(*)m+1-[2m/k]^(*);***,we show that the isolated toughness bound is tight.

关键词： graph isolated toughness fractional k-factor fractional(k,m)-deleted graph